This paper compares the performance of popular estimators of teacher quality, which serve as inputs into teacher incentive schemes. I model an administrator tasked with categorizing teachers with respect to an exogenous cutoff, showing that the preferred estimator depends on the relationship between teacher quality and class size. I then use data from Los Angeles to show that the simpler fixed effects estimator would outperform the more popular empirical Bayes estimator, meaning that the administrator would prefer to use it to either reward high-performing teachers or sanction low-performing ones. The preferred estimator would create 200 fewer classification errors.